DEPR: DTA(float_data, dtype=dt64tz) (#49361)

DEPR: DTA.astype unitless, DTA(float_data, dtype=dt64tz)
pandas-dev · Nov 2, 2022 · 4e7ade7 · 4e7ade7
1 parent fb9b345
commit 4e7ade7
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diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
@@ -298,6 +298,7 @@ Removal of prior version deprecations/changes
 - Changed the behavior of :class:`Series` constructor, it will no longer infer a datetime64 or timedelta64 dtype from string entries (:issue:`41731`)
 - Changed behavior of :class:`Timestamp` constructor with a ``np.datetime64`` object and a ``tz`` passed to interpret the input as a wall-time as opposed to a UTC time (:issue:`42288`)
 - Changed behavior of :class:`Index` constructor when passed a ``SparseArray`` or ``SparseDtype`` to retain that dtype instead of casting to ``numpy.ndarray`` (:issue:`43930`)
+- Changed behavior of :class:`Index`, :class:`Series`, :class:`DataFrame` constructors with floating-dtype data and a :class:`DatetimeTZDtype`, the data are now interpreted as UTC-times instead of wall-times, consistent with how integer-dtype data are treated (:issue:`45573`)
 - Removed the deprecated ``base`` and ``loffset`` arguments from :meth:`pandas.DataFrame.resample`, :meth:`pandas.Series.resample` and :class:`pandas.Grouper`. Use ``offset`` or ``origin`` instead (:issue:`31809`)
 - Changed behavior of :meth:`DataFrame.any` and :meth:`DataFrame.all` with ``bool_only=True``; object-dtype columns with all-bool values will no longer be included, manually cast to ``bool`` dtype first (:issue:`46188`)
 - Changed behavior of comparison of a :class:`Timestamp` with a ``datetime.date`` object; these now compare as un-equal and raise on inequality comparisons, matching the ``datetime.datetime`` behavior (:issue:`36131`)

diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
@@ -2230,27 +2230,10 @@ def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None):
         return data, copy
 
     if is_float_dtype(data.dtype):
-        # Note: we must cast to datetime64[ns] here in order to treat these
-        #  as wall-times instead of UTC timestamps.
-        data = data.astype(DT64NS_DTYPE)
+        # pre-2.0 we treated these as wall-times, inconsistent with ints
+        # GH#23675, GH#45573 deprecated to treat symmetrically with integer dtypes
+        data = data.astype(np.int64)
         copy = False
-        if (
-            tz is not None
-            and len(data) > 0
-            and not timezones.is_utc(timezones.maybe_get_tz(tz))
-        ):
-            # GH#23675, GH#45573 deprecate to treat symmetrically with integer dtypes
-            warnings.warn(
-                "The behavior of DatetimeArray._from_sequence with a timezone-aware "
-                "dtype and floating-dtype data is deprecated. In a future version, "
-                "this data will be interpreted as nanosecond UTC timestamps "
-                "instead of wall-times, matching the behavior with integer dtypes. "
-                "To retain the old behavior, explicitly cast to 'datetime64[ns]' "
-                "before passing the data to pandas. To get the future behavior, "
-                "first cast to 'int64'.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
 
     elif is_timedelta64_dtype(data.dtype) or is_bool_dtype(data.dtype):
         # GH#29794 enforcing deprecation introduced in GH#23539

diff --git a/pandas/tests/dtypes/test_missing.py b/pandas/tests/dtypes/test_missing.py
@@ -426,11 +426,9 @@ def test_array_equivalent(dtype_equal):
         dtype_equal=dtype_equal,
     )
 
-    msg = "will be interpreted as nanosecond UTC timestamps instead of wall-times"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        dti1 = DatetimeIndex([0, np.nan], tz="US/Eastern")
-        dti2 = DatetimeIndex([0, np.nan], tz="CET")
-        dti3 = DatetimeIndex([1, np.nan], tz="US/Eastern")
+    dti1 = DatetimeIndex([0, np.nan], tz="US/Eastern")
+    dti2 = DatetimeIndex([0, np.nan], tz="CET")
+    dti3 = DatetimeIndex([1, np.nan], tz="US/Eastern")
 
     assert array_equivalent(
         dti1,
@@ -444,7 +442,7 @@ def test_array_equivalent(dtype_equal):
     )
     # The rest are not dtype_equal
     assert not array_equivalent(DatetimeIndex([0, np.nan]), dti1)
-    assert not array_equivalent(
+    assert array_equivalent(
         dti2,
         dti1,
     )

diff --git a/pandas/tests/series/methods/test_astype.py b/pandas/tests/series/methods/test_astype.py
@@ -395,10 +395,7 @@ def test_astype_nan_to_bool(self):
     def test_astype_ea_to_datetimetzdtype(self, dtype):
         # GH37553
         ser = Series([4, 0, 9], dtype=dtype)
-        warn = FutureWarning if ser.dtype.kind == "f" else None
-        msg = "with a timezone-aware dtype and floating-dtype data"
-        with tm.assert_produces_warning(warn, match=msg):
-            result = ser.astype(DatetimeTZDtype(tz="US/Pacific"))
+        result = ser.astype(DatetimeTZDtype(tz="US/Pacific"))
 
         expected = Series(
             {
@@ -408,21 +405,6 @@ def test_astype_ea_to_datetimetzdtype(self, dtype):
             }
         )
 
-        if dtype in tm.FLOAT_EA_DTYPES:
-            expected = Series(
-                {
-                    0: Timestamp(
-                        "1970-01-01 00:00:00.000000004-08:00", tz="US/Pacific"
-                    ),
-                    1: Timestamp(
-                        "1970-01-01 00:00:00.000000000-08:00", tz="US/Pacific"
-                    ),
-                    2: Timestamp(
-                        "1970-01-01 00:00:00.000000009-08:00", tz="US/Pacific"
-                    ),
-                }
-            )
-
         tm.assert_series_equal(result, expected)
 
     def test_astype_retain_Attrs(self, any_numpy_dtype):